Predicting Oxidation Potentials with DFT-Driven Machine Learning

We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (<i>E</i>_HOMO), OxPot serves as a robust platform for accelerating the prediction of oxidation potential (<i>E</i>_ox). Using the PBE0 hybrid functional and cc-pVDZ basis set, we establish a strong near-linear correlation between <i>E</i>_HOMO and experimental <i>E</i>_ox values, achieving an exceptional correlation coefficient (<i>R</i>²) of 0.977 and a low root-mean-square error (RMSE) of 0.064. The correlation highlights the accuracy of OxPot as a machine learning (ML)-ready resource for <i>E</i>_ox prediction. To further facilitate future development of ML models, we extensively tested various algorithms and conducted a thorough feature importance analysis. This analysis offers valuable insights into the key molecular descriptors that influence <i>E</i>_ox predictions, thereby enhancing model interpretability and guiding the design of more effective predictive models. Furthermore, the computational efficiency of the methodology ensures rapid predictions of <i>E</i>_ox for additional chemically similar molecules, thereby increasing its applicability for large-scale molecular screening and broader applications in chemical research.